Weather forecasts are generally produced using a dynamic model of weather and climate evolution. Modern weather forecasts are generated using weather ensembles. A weather ensemble is a set of plausible weather forecasts based on a plurality of initial conditions and/or physical models.
Generally, weather forecasts are generated using a process known as data assimilation which makes use of measurements taken over a period of 24 hours to generate a plurality of initial conditions. Each initial condition is then used to generate plausible weather scenarios or ensemble members. The ensemble members are then combined into one weather forecast. The combination of ensemble members into a weather forecast may comprise using the average temperatures of the ensemble members, the most common temperature of the forecasts, or other modeling approaches. The ensembles are also used to determine a chance of precipitation. For example, if 20% of the ensemble members contain precipitation, a weather forecast may state that there is a 20% chance of precipitation.
Modern forecasting models suffer from a few main issues. First, the models suffer from persistent biases. Modern forecasting models make a series of approximation in order to simulate physical processes. For example, many modeling approaches use a coarse grid to approximate the spatial correlation of weather. While the approximation may be useful, the grids are often too coarse to resolve important physical processes such as convection. Second, the models tend to suffer from under-dispersion of the ensemble, often caused by an underestimation of the magnitude of systematic errors stemming from the broad approximations discussed above. Finally, modern modeling approaches only use a number of ensemble members necessary to make a probable forecast. By using a small number of ensemble members, the modern approaches fail to capture a significant fraction of potential weather outcomes. Thus, these models are inefficient at determining the risks of rare events occurring.
Another issue with modern forecasting models is that they generate weather forecasts at a generalized scale. For example, a weather forecast for a single day may include various forecasts for each city. While general forecasts are useful to a large portion of the population, farmers may require locally tailored forecasts which can predict the temperatures and likely precipitation for their individual fields. Additionally, agronomic models may require more localized temperature predictions to make accurate predictions, such as the yield of a specific crop, or good recommendations, such as the best days to harvest a crop.